Are you suffering from data quality delusions?

One of the biggest challenges – as data quality professionals we make assumptions about other people that hinder our ability to put together a compelling business case.

How many of these assumptions do you make?

Everyone understands how important data quality is

Everyone wants to do something to resolve data quality issues

Everyone knows what they should be doing to solve data quality problems.

We don;t need quality data for big data analytics

When we make these assumptions it is easy to put together a weak business case, assuming that key business decision makers will understand the value.

In practice, the link between poor quality data and poor business performance is typically not well understood.

A strong business case for data quality will not suffer these delusions.

Rather, it should spell out the link between poor quality data and business issues.

Having trouble with outstanding invoices?

What percentage of invoices are not paid due to errors in key data? What is the average size of unpaid invoices? For how many days do you carry the extra debt? How much does that cost you? What is the administrative cost of correcting and resubmitting each invoice.

Data quality should be defined as data that supports the required business goals. Tools help to expose data anomalies so that the impact can be quantified with business stakeholders. This approach helps business to visualize how poor quality data is impacting key processes, and also to dismiss anomalies that have no (or minimal) impact so that these don’t skew the business case. Concrete metrics make for a strong business case.